Predicting COVID-19 Vaccination Decision-Making Profiles Among Dutch Adults: From Survey to National Administrative Data.

Publication date: May 21, 2026

Individual’s vaccination behaviors are influenced by factors such as values and beliefs. Applying latent class analysis (LCA) to such factors from the Longitudinal Internet Studies for the Social Sciences (LISS) panel[1], Matthijssen et al. [2] identified 12 distinct COVID-19 vaccination decision-making profiles in a sample of 2,567 Dutch adults. However, the extent to which membership in these profiles can be predicted using sociodemographic administrative data remains unknown. We assessed that by linking survey data to administrative records from the Statistics Netherlands (CBS) database and employing an Explainable Boosting Machine (EBM) model. The model showed substantial predictive performance and revealed the central role of income, interacting with other variables, in predicting profile membership.

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Concepts Keywords
Dutch Adult
Sociodemographic COVID-19
Stud COVID-19
Vaccination COVID-19 Vaccines
COVID-19 Vaccines
Decision Making
Female
Humans
Machine Learning
Male
Middle Aged
Netherlands
Registry-based study
SARS-CoV-2
Surveys and Questionnaires
Vaccination

Semantics

Type Source Name
disease MESH COVID-19
disease MESH LCA
disease MESH EBM
disease MESH MAE

Original Article

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